This study aims to quantify the influence of the amount of cement and chloride salt on the unconfined compression strength （UCS） of Lianyungang marine clay. The clays with various sodium chloride salt concentrations...This study aims to quantify the influence of the amount of cement and chloride salt on the unconfined compression strength （UCS） of Lianyungang marine clay. The clays with various sodium chloride salt concentrations were prepared artificially and stabilized by ordinary Portland cement with various contents. A series of UCS tests of cement stabilized clay specimen after 28 d curing were carried out. The results indicate that the increase of salt concentration results in the decrease in the UCS of cement-treated soil. The negative effect of salt concentration on the strength of cement stabilized clay directly relates to the cement content and salt concentration. The porosity-salt concentration/cement content ratio is a fundamental parameter for assessing the UCS of cement-treated salt-rich clay. An empirical prediction model of UCS is also proposed to take into account the effect of salt concentration. The findings of this study can be referenced for the stabilization improvement of chloride slat- rich soft clay.展开更多
New-type magnesium alloy with prominent solubility and mechanical property lays foundation for preparing fracturing part in petroleum extraction.Herein,Mg-xZn-Zr-SiC alloy is prepared with casting strategy.Electrochem...New-type magnesium alloy with prominent solubility and mechanical property lays foundation for preparing fracturing part in petroleum extraction.Herein,Mg-xZn-Zr-SiC alloy is prepared with casting strategy.Electrochemical and compression tests are conducted to assess the feasibility as decomposable material.Morphology,composition,phase and distribution are characterized to investigate decomposition mechanism.Results indicate that floccule,substrate component and reticulate secondary phase are formed on as-prepared surface.Sample also acts out enhanced compression strength to maintain pressure and guarantee stability in dissolution process.Furthermore,as decomposition time and zinc content increase,couple corrosion intensifies,resulting in gradually enhanced decomposition rate.Rapid sample decomposition is mainly due to basal anode dissolution,micro particle exfoliation and poor decomposition resistance of corroding product.Such work shows profound significance in preparing new-type accessible alloy to ensure rapid dissolution of fracturing part and guarantee stable compression strength in oil-gas reservoir exploitation.展开更多
Quantitatively correcting the unconfined compressive strength for sample disturbance is an important research project in the practice of ocean engineering and geotechnical engineering. In this study, the specimens of ...Quantitatively correcting the unconfined compressive strength for sample disturbance is an important research project in the practice of ocean engineering and geotechnical engineering. In this study, the specimens of undisturbed natural marine clay obtained from the same depth at the same site were deliberately disturbed to different levels. Then, the specimens with different extents of sample disturbance were trimmed for both oedometer tests and unconfined compression tests. The degree of sample disturbance SD is obtained from the oedometer test data. The relationship between the unconfined compressive strength q u and SD is studied for investigating the effect of sample disturbance on q u. It is found that the value of q u decreases linearly with the increase in SD. Then, a simple method of correcting q u for sample disturbance is proposed. Its validity is also verified through analysis of the existing published data.展开更多
Rubberized concrete is one of the most studied applications of discarded tires and offers a promising approach to developing materials with enhanced properties.The rubberized concrete mixture results in a reduced modu...Rubberized concrete is one of the most studied applications of discarded tires and offers a promising approach to developing materials with enhanced properties.The rubberized concrete mixture results in a reduced modulus of elasticity and a reduced compressive and tensile strength compared to traditional concrete.This study employs finite element simulations to investigate the elastic properties of rubberized mortar(RuM),considering the influence of inclusion stiffness and interfacial debonding.Different homogenization schemes,including Voigt,Reuss,and mean-field approaches,are implemented using DIGIMAT and ANSYS.Furthermore,the influence of the interfacial transition zone(ITZ)between mortar and rubber is analyzed by periodic homogenization.Subsequently,the influence of the ITZ is examined through a linear fracture analysis with the stress intensity factor as a key parameter,using the ANSYS SMART crack growth tool.Finally,a non-linear study in FEniCS is carried out to predict the strength of the composite material through a compression test.Comparisons with high density polyethylene(HDPE)and gravel inclusions show that increasing inclusion stiffness enhances compressive strength far more effectively than simply improving the mortar/rubber bond.Indeed,when the inclusions are much softer than the surrounding matrix,any benefit gained on the elastic modulus or strength from stronger interfacial adhesion becomes almost negligible.This study provide numerical evidence that tailoring the rubber’s intrinsic stiffness—not merely strengthening the rubber/mortar interface—is a decisive factor for improving the mechanical performance of RuM.展开更多
Three types of activators such as sodium hydroxide,calcium oxide and triethanolamine(TEA)are used to establish different activation environments to address the problems associated with the process of activating fly as...Three types of activators such as sodium hydroxide,calcium oxide and triethanolamine(TEA)are used to establish different activation environments to address the problems associated with the process of activating fly ash paste.We conducted mechanical tests and numerical simulations to understand the evolution of microstructure,and used environmental scanning electron microscopy(ESEM)and energy dispersive spectroscopy(EDS)techniques to analyze the microenvironments of the samples.The mechanical properties of fly ash paste under different activation conditions and the changes in the microstructure and composition were investigated.The results revealed that under conditions of low NaOH content(1%-3%),the strength of the sample increased significantly.When the content exceeded 4%,the rate of increase in strength decreased.Based on the results,the optimal NaOH content was identified,which was about 4%.A good activation effect,especially for short-term activation(3-7 d),was achieved using TEA under high doping conditions.The activation effect was poor for long-term strength after 28 days.The CaO content did not significantly affect the degree of activation achieved.The maximum effect was exerted when the content of CaO was 2%.The virtual cement and concrete testing laboratory(VCCTL)was used to simulate the hydration process,and the results revealed that the use of the three types of activators accelerated the formation of Ca(OH)_(2) in the system.The activators also corroded the surface of the fly ash particles,resulting in a pozzolanic reaction.The active substances in fly ash were released efficiently,and hydration was realized.The pores were filled with hydration products,and the microstructure changed to form a new frame of paste filling that helped improve the strength of fly ash paste.展开更多
In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive streng...In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive strength.In this study,505 groups of data were collected,and a new database of compressive strength of PFGC was constructed.In order to establish an accurate prediction model of compressive strength,five different types of machine learning networks were used for comparative analysis.The five machine learning models all showed good compressive strength prediction performance on PFGC.Among them,R2,MSE,RMSE and MAE of decision tree model(DT)are 0.99,1.58,1.25,and 0.25,respectively.While R2,MSE,RMSE and MAE of random forest model(RF)are 0.97,5.17,2.27 and 1.38,respectively.The two models have high prediction accuracy and outstanding generalization ability.In order to enhance the interpretability of model decision-making,we used importance ranking to obtain the perception of machine learning model to 13 variables.These 13 variables include chemical composition of fly ash(SiO_(2)/Al_(2)O_(3),Si/Al),the ratio of alkaline liquid to the binder,curing temperature,curing durations inside oven,fly ash dosage,fine aggregate dosage,coarse aggregate dosage,extra water dosage and sodium hydroxide dosage.Curing temperature,specimen ages and curing durations inside oven have the greatest influence on the prediction results,indicating that curing conditions have more prominent influence on the compressive strength of PFGC than ordinary Portland cement concrete.The importance of curing conditions of PFGC even exceeds that of the concrete mix proportion,due to the low reactivity of pure fly ash.展开更多
Traditional machine learning(ML)encounters the challenge of parameter adjustment when predicting the compressive strength of reclaimed concrete.To address this issue,we introduce two optimized hybrid models:the Bayesi...Traditional machine learning(ML)encounters the challenge of parameter adjustment when predicting the compressive strength of reclaimed concrete.To address this issue,we introduce two optimized hybrid models:the Bayesian optimization model(B-RF)and the optimal model(Stacking model).These models are applied to a data set comprising 438 observations with five input variables,with the aim of predicting the compressive strength of reclaimed concrete.Furthermore,we evaluate the performance of the optimized models in comparison to traditional machine learning models,such as support vector regression(SVR),decision tree(DT),and random forest(RF).The results reveal that the Stacking model exhibits superior predictive performance,with evaluation indices including R2=0.825,MAE=2.818 and MSE=14.265,surpassing the traditional models.Moreover,we also performed a characteristic importance analysis on the input variables,and we concluded that cement had the greatest influence on the compressive strength of reclaimed concrete,followed by water.Therefore,the Stacking model can be recommended as a compressive strength prediction tool to partially replace laboratory compressive strength testing,resulting in time and cost savings.展开更多
Introduction It is necessary for an ideal bioceramic scaffold to have a suitable structure.The structure can affect the mechanical properties of the scaffold(i.e.,elastic modulus and compressive strength)and the biolo...Introduction It is necessary for an ideal bioceramic scaffold to have a suitable structure.The structure can affect the mechanical properties of the scaffold(i.e.,elastic modulus and compressive strength)and the biological properties of the scaffold(i.e.,degradability and cell growth rate).Lattice structure is a kind of periodic porous structure,which has some advantages of light weight and high strength,and is widely used in the preparation of bioceramic scaffolders.For the structure of the scaffold,high porosity and large pore size are important for bone growth,bone integration and promoting good mechanical interlocking between neighboring bones and the scaffold.However,scaffolds with a high porosity often lack mechanical strength.In addition,different parts of the bone have different structural requirements.In this paper,scaffolds with a non-uniform structure or a hierarchical structure were designed,with loose and porous exterior to facilitate cell adhesion,osteogenic differentiation and vascularization as well as relatively dense interior to provide sufficient mechanical support for bone repair.Methods In this work,composite ceramics scaffolds with 10%akermanite content were prepared by DLP technology.The scaffold had a high porosity outside to promote the growth of bone tissue,and a low porosity inside to withstand external forces.The compressive strength,fracture form,in-vitro degradation performance and bioactivity of graded bioceramic scaffolds were investigated.The models of scaffolds were imported into the DLP printer with a 405 nm light.The samples were printed with the intensity of 8 mJ/cm^(2)and a layer thickness of 50μm.Finally,the ceramic samples were sintered at 1100℃.The degradability of the hierarchical gyroid bioceramic scaffolds was evaluated through immersion in Tris-HCl solution and SBF solution at a ratio of 200 mL/g.The bioactivity of bioceramic was obtained via immersing them in SBF solution for two weeks.The concentrations of calcium,phosphate,silicon,and magnesium ions in the soaking solution were determined by an inductively coupled plasma optical emission spectrometer.Results and discussion In this work,a hierarchical Gyroid structure HA-AK10 scaffold(sintered at 1100℃)with a radial internal porosity of 50%and an external porosity of 70%is prepared,and the influence of structural form on the compressive strength and degradation performance of the scaffold is investigated.The biological activity of the bioceramics in vitro is also verified.The mechanical simulation results show that the stress distribution corresponds to the porosity distribution of the structure,and the low porosity is larger and the overall stress concentration phenomenon does not appear.After soaking in SBF solution,Si—OH is firstly formed on the surface of bioceramics,and then silicon gel layer is produced due to the presence of calcium and silicon ions.The silicon gel layer is dissociated into negatively charged groups under alkaline environment secondary adsorption of calcium ions and phosphate ions,forming amorphous calcium phosphate,and finally amorphous calcium phosphate crystals and adsorption of carbonate ions,forming carbonate hydroxyapatite.This indicates that the composite bioceramics have a good biological activity in-vitro and can provide a good environment for the growth of bone cells.A hierarchical Gyroid ceramic scaffold with a bone geometry is prepared via applying the hierarchical structure to the bone contour scaffold.The maximum load capacity of the hierarchical Gyroid ceramic scaffold is 8 times that of the uniform structure.Conclusions The hierarchical structure scaffold designed had good overall compressive performance,good degradation performance,and still maintained a good mechanical stability during degradation.In addition,in-vitro biological experimental results showed that the surface graded composite scaffold could have a good in-vitro biological activity and provide a good environment for bone cells.Compared to the heterosexual structure,the graded scaffold had greater mechanical properties.展开更多
Microwave-curing and mechanical grinding of fly ash have both beenadopted as effective methods for improving the early-age strength of alkali-activated fly ash(AAFA)binders.This study combined these two approaches by ...Microwave-curing and mechanical grinding of fly ash have both beenadopted as effective methods for improving the early-age strength of alkali-activated fly ash(AAFA)binders.This study combined these two approaches by synthesizing AAFA using original,medium-fine,and ultrafine fly ash as precursors,and then specimens were cured with a five-stage temperature-controlled microwave.The compressive strength results indicate that the original AAFA develops the highest strength initially during microwave-curing,reaching 28 MPa at stage 2.Medium-fine AAFA exhibits the highest strength of 60 MPa when cured to stage 4-I,which is 26%higher than the peak strength of original AAFA.It is attributed to the significant rise in their specific surface area,which accelerates the dissolution of Si and Al from the precursor and facilitates the subsequent formation of N-A-S-H gels.Additionally,nanoscale zeolite crystals formed as secondary products fill the tiny gaps between amorphous products,thereby significantly improving their microstructure.In contrast,ultrafine fly ash,primarily composed of fragmented particles,necessitated a substantial amount of water,which adversely affects the absorption efficiency for microwave of AAFA specimens.Thus,ultrafine AAFA specimens consistently exhibit the lowest compressive strength.Specifically,at the end of curing,the compressive strength of these three specimens with microwave-curing is approximately 32%,59%,and 172%higher than that of the steam-cured sample,respectively.These findings demonstrate the compatibility of microwave-curing and fly ash refinement in enhancing the early compressive strength development of AAFA.展开更多
The compressive strength of the pellets is a key indicator that determines the production efficiency in straight grate.It usually relies on manual sampling and testing,which is cumbersome and inefficient.To address th...The compressive strength of the pellets is a key indicator that determines the production efficiency in straight grate.It usually relies on manual sampling and testing,which is cumbersome and inefficient.To address this,a time series prediction model for pellet compressive strength was developed,combining a gradient boosting decision tree with a temporal convolutional network(GBDT-TCN).Firstly,the key physical characteristics of the pellet production process were established through the feature construction method,and then the multicollinear features were eliminated based on the Spearman correlation coefficient.The final selection of feature parameters,amounting to 9,was determined using recursive feature elimination(RFE)method.Finally,the GBDT algorithm was used to establish the nonlinear relationship between these features and the compressive strength.The GBDT prediction results and process data were constructed into a time series dataset,which was input into the TCN unit cascade model.The time series information was captured through the distribution coefficient of the loss function in the time series.Results illustrate that the GBDT-TCN method proposed performs well in the task of predicting the compressive strength of pellets.Compared with the prediction model using only GBDT,the accuracy within±100 N is increased from 83.33%to 90.00%.展开更多
With the growing demand for sustainable development in the mining industry,cemented paste backfill(CPB)materials,primarily composed of tailings,play a crucial role in mine backfilling and underground support systems.T...With the growing demand for sustainable development in the mining industry,cemented paste backfill(CPB)materials,primarily composed of tailings,play a crucial role in mine backfilling and underground support systems.To enhance the mechanical properties of CPB materials,fiber reinforcement technology has gradually gained attention,though challenges remain in predicting its performance.This study develops a hybrid model based on the adaptive equilibrium optimizer(adap-EO)-enhanced XGBoost method for accurately predicting the uniaxial compressive strength of fiber-reinforced CPB.Through systematic comparison with various other machine learning methods,results demonstrate that the proposed hybridmodel exhibits excellent predictive performance on the test set,achieving a coefficient of determination(R^(2))of 0.9675,root mean square error(RMSE)of 0.6084,and mean absolute error(MAE)of 0.4620.Input importance analysis reveals that cement-tailings ratio,curing time,and concentration are the three most critical factors affectingmaterial strength,with cement-tailings ratio showing a positive correlation with strength,concentrations above 70% significantly improvingmaterial strength,and curing periods beyond 28 days being essential for strength development.Fiber parameters contribute secondarily but notably to material strength,with fiber length exhibiting an optimal range of approximately 12 mm.This study not only provides a high-precision strength prediction model but also reveals the inherent correlations between various parameters and material performance,offering scientific basis for mixture optimization and engineering applications of fiber-reinforced CPB materials.展开更多
Soilcrete is a composite material of soil and cement that is highly valued in the construction industry.Accurate measurement of its mechanical properties is essential,but laboratory testing methods are expensive,timec...Soilcrete is a composite material of soil and cement that is highly valued in the construction industry.Accurate measurement of its mechanical properties is essential,but laboratory testing methods are expensive,timeconsuming,and include inaccuracies.Machine learning(ML)algorithms provide a more efficient alternative for this purpose,so after assessment with a statistical extraction method,ML algorithms including back-propagation neural network(BPNN),K-nearest neighbor(KNN),radial basis function(RBF),feed-forward neural networks(FFNN),and support vector regression(SVR)for predicting the uniaxial compressive strength(UCS)of soilcrete,were proposed in this study.The developed models in this study were optimized using an optimization technique,gradient descent(GD),throughout the analysis(direct optimization for neural networks and indirect optimization for other models corresponding to their hyperparameters).After doing laboratory analysis,data pre-preprocessing,and data-processing analysis,a database including 600 soilcrete specimens was gathered,which includes two different soil types(clay and limestone)and metakaolin as a mineral additive.80%of the database was used for the training set and 20%for testing,considering eight input parameters,including metakaolin content,soil type,superplasticizer content,water-to-binder ratio,shrinkage,binder,density,and ultrasonic velocity.The analysis showed that most algorithms performed well in the prediction,with BPNN,KNN,and RBF having higher accuracy compared to others(R^(2)=0.95,0.95,0.92,respectively).Based on this evaluation,it was observed that all models show an acceptable accuracy rate in prediction(RMSE:BPNN=0.11,FFNN=0.24,KNN=0.05,SVR=0.06,RBF=0.05,MAD:BPNN=0.006,FFNN=0.012,KNN=0.008,SVR=0.006,RBF=0.009).The ML importance ranking-sensitivity analysis indicated that all input parameters influence theUCS of soilcrete,especially the water-to-binder ratio and density,which have themost impact.展开更多
This study focuses on empirical modeling of the strength characteristics of urban soils contaminated with heavy metals using machine learning tools and their subsequent stabilization with ordinary Portland cement(OPC)...This study focuses on empirical modeling of the strength characteristics of urban soils contaminated with heavy metals using machine learning tools and their subsequent stabilization with ordinary Portland cement(OPC).For dataset collection,an extensive experimental program was designed to estimate the unconfined compressive strength(Qu)of heavy metal-contaminated soils collected from awide range of land use pattern,i.e.residential,industrial and roadside soils.Accordingly,a robust comparison of predictive performances of four data-driven models including extreme learning machines(ELMs),gene expression programming(GEP),random forests(RFs),and multiple linear regression(MLR)has been presented.For completeness,a comprehensive experimental database has been established and partitioned into 80%for training and 20%for testing the developed models.Inputs included varying levels of heavy metals like Cd,Cu,Cr,Pb and Zn,along with OPC.The results revealed that the GEP model outperformed its counterparts:explaining approximately 96%of the variability in both training(R2=0.964)and testing phases(R^(2)=0.961),and thus achieving the lowest RMSE and MAE values.ELM performed commendably but was slightly less accurate than GEP whereas MLR had the lowest performance metrics.GEP also provided the benefit of traceable mathematical equation,enhancing its applicability not just as a predictive but also as an explanatory tool.Despite its insights,the study is limited by its focus on a specific set of heavy metals and urban soil samples of a particular region,which may affect the generalizability of the findings to different contamination profiles or environmental conditions.The study recommends GEP for predicting Qu in heavy metal-contaminated soils,and suggests further research to adapt these models to different environmental conditions.展开更多
Lumbar degeneration leads to changes in geometry and density distribution of vertebrae,which could further influence the mechanical property and behavior.This study aimed to quantitatively describe the variations in s...Lumbar degeneration leads to changes in geometry and density distribution of vertebrae,which could further influence the mechanical property and behavior.This study aimed to quantitatively describe the variations in shape and density distribution for degenerated vertebrae by statistical models,and utilized the specific statistical shape model(SSM)/statistical appearance model(SAM)modes to assess compressive strength and fracture behavior.Highly detailed SSM and SAM were developed based on the 75 L1 vertebrae of elderly men,and their variations in shape and density distribution were quantified with principal component(PC)modes.All vertebrae were classified into mild(n=22),moderate(n=29),and severe(n=24)groups according to the overall degree of degeneration.Quantitative computed tomography-based finite element analysis was used to calculate compressive strength for each L1 vertebra,and the associations between compressive strength and PC modes were evaluated by multivariable linear regression(MLR).Moreover,the distributions of equivalent plastic strain(PEEQ)for the vertebrae assigned with the first modes of SSM and SAM at mean±3SD were investigated.The Leave-One-Out analysis showed that our SSM and SAM had good performance,with mean absolute errors of 0.335±0.084 mm and 64.610±26.620 mg/cm3,respectively.A reasonable accuracy of bone strength prediction was achieved by using four PC modes(SSM 1,SAM 1,SAM 4,and SAM 5)to construct the MLR model.Furthermore,the PEEQ values were more sensitive to degeneration-related variations of density distribution than those of morphology.The density variations may change the deformity type(compression deformity or wedge deformity),which further affects the fracture pattern.Statistical models can identify the morphology and density variations in degenerative vertebrae,and the SSM/SAM modes could be used to assess compressive strength and fracture behavior.The above findings have implications for assisting clinicians in pathological diagnosis,fracture risk assessment,implant design,and preoperative planning.展开更多
Foam concrete is widely used in engineering due to its lightweight and high porosity.Its compressive strength,a key performance indicator,is influenced by multiple factors,showing nonlinear variation.As compressive st...Foam concrete is widely used in engineering due to its lightweight and high porosity.Its compressive strength,a key performance indicator,is influenced by multiple factors,showing nonlinear variation.As compressive strength tests for foam concrete take a long time,a fast and accurate prediction method is needed.In recent years,machine learning has become a powerful tool for predicting the compressive strength of cement-based materials.However,existing studies often use a limited number of input parameters,and the prediction accuracy of machine learning models under the influence of multiple parameters and nonlinearity remains unclear.This study selects foam concrete density,water-to-cement ratio(W/C),supplementary cementitious material replacement rate(SCM),fine aggregate to binder ratio(FA/Binder),superplasticizer content(SP),and age of the concrete(Age)as input parameters,with compressive strength as the output.Five different machine learning models were compared,and sensitivity analysis,based on Shapley Additive Explanations(SHAP),was used to assess the contribution of each input parameter.The results show that Gaussian Process Regression(GPR)outperforms the other models,with R2,RMSE,MAE,and MAPE values of 0.95,1.6,0.81,and 0.2,respectively.It is because GPR,optimized through Bayesian methods,better fits complex nonlinear relationships,especially considering a large number of input parameters.Sensitivity analysis indicates that the influence of input parameters on compressive strength decreases in the following order:foam concrete density,W/C,Age,FA/Binder,SP,and SCM.展开更多
Against the background of“carbon peak and carbon neutrality,”it is of great practical significance to develop non-blast furnace ironmaking technology for the sustainable development of steel industry.Carbon-bearing ...Against the background of“carbon peak and carbon neutrality,”it is of great practical significance to develop non-blast furnace ironmaking technology for the sustainable development of steel industry.Carbon-bearing iron ore pellet is an innovative burden of direct reduction ironmaking due to its excellent self-reducing property,and the thermal strength of pellet is a crucial metallurgical property that affects its wide application.The carbon-bearing iron ore pellet without binders(CIPWB)was prepared using iron concentrate and anthracite,and the effects of reducing agent addition amount,size of pellet,reduction temperature and time on the thermal compressive strength of CIPWB during the reduction process were studied.Simultaneously,the mechanism of the thermal strength evolution of CIPWB was revealed.The results showed that during the low-temperature reduction process(300-500℃),the thermal compressive strength of CIPWB linearly increases with increasing the size of pellet,while it gradually decreases with increasing the anthracite ratio.When the CIPWB with 8%anthracite is reduced at 300℃for 60 min,the thermal strength of pellet is enhanced from 13.24 to 31.88 N as the size of pellet increases from 8.04 to 12.78 mm.Meanwhile,as the temperature is 500℃,with increasing the anthracite ratio from 2%to 8%,the thermal compressive strength of pellet under reduction for 60 min remarkably decreases from 41.47 to 8.94 N.Furthermore,in the high-temperature reduction process(600-1150℃),the thermal compressive strength of CIPWB firstly increases and then reduces with increasing the temperature,while it as well as the temperature corresponding to the maximum strength decreases with increasing the anthracite ratio.With adding 18%anthracite,the thermal compressive strength of pellet reaches the maximum value at 800℃,namely 35.00 N,and obtains the minimum value at 1050℃,namely 8.60 N.The thermal compressive strength of CIPWB significantly depends on the temperature,reducing agent dosage,and pellet size.展开更多
Microbially induced carbonate precipitation(MICP)is an eco-friendly soil improvement technique.However,this method still has some drawbacks,such as low conversion efficiency of CaCO_(3) crystallization,insufficient st...Microbially induced carbonate precipitation(MICP)is an eco-friendly soil improvement technique.However,this method still has some drawbacks,such as low conversion efficiency of CaCO_(3) crystallization,insufficient strength for certain applications,and requiring multiple treatments.Previous studies have re-ported that sticky rice can regulate CaCO_(3) crystals(i.e.,chemical CaCO_(3))in the sticky rice-lime mortar,showing potential for improving the bio-cementation.Therefore,this study explored the possibility of using sticky rice to enhance the biocementation effect.Tests were carried out to assess the strength and perme-ability of bio-cemented sand with the inclusion of sticky rice.The results indicated that sticky rice may regulate the type and size of bio-CaCO_(3) crystals,and the use of an appropriate amount of sticky rice as additive could increase the strength of sand columns by regulating CaCO_(3) crystallization.Polyhedral calcites may be more favourable for the increasing strength than some vaterites with a hollow spherical structure.The combination of MICP and sticky rice can significantly decrease the coefficient of permeability to a value that was much lower than that by using sticky rice and MICP alone.Bio-CaCO_(3) immobilized the sticky rice on one end on sand particles,and the reticulated structure of sticky rice divided large pores into small pores,which may be the important cause of the decrease in permeability coefficient.Finally,this study proposed that the MICP with the sticky rice as an additive may enhance the MICP effect and prevent the surface erosion of coarse-grained sand slopes.展开更多
This study investigates the effect of different in situ conditions like flaw infill,heat-treatment temperatures,and sample porosities on the anisotropic compressive response of jointed samples with an impersistent fla...This study investigates the effect of different in situ conditions like flaw infill,heat-treatment temperatures,and sample porosities on the anisotropic compressive response of jointed samples with an impersistent flaw.Jointed samples of different porosities are prepared by mixing Plaster of Paris(POP)with different water contents,i.e.60%(i.e.for lower porosity)and 80%(i.e.for higher porosity).These samples are grouted with different infill materials,i.e.un-grouted,cement and sand-cement(3:1)-bio-concrete(SCB)mix and subsequently subjected to different temperatures,i.e.100℃,200℃ and 300℃.The results reveal the distinct stages in the stress-strain responses of samples characterized by initial micro-cracks closure,elastic transition,and non-linear response till peak followed by a post-peak behaviour.The un-grouted samples exhibit their lowest strength at 30°joint orientation.The ratios of maximum to minimum strength are 3.11 and 3.22 with varying joint orientations for lower and higher porosity samples,respectively.Strengths of cement and SCB mix grouted samples are increased for all joint orientations ranging between 16.13%-69.83%and 18.04%-73%at low porosity and 22%-48.66%and 27.77%-51.57%at high porosity,respectively as compared to the un-grouted samples.However,the strength of the grouted samples is decreased by 66.94%-75.47%and 77.17%-81.05%at lower porosity,and 79.37%-82.86%and 81.29%-95.55%at higher porosity for cement and for SCB grouts with an increase in the heating temperature from 30℃ to 300℃,respectively.These observations could be due to the suppression of favourable crack initiation locations,i.e.flaw tips along the samples due to the filling of the crack by grouting and generation of thermal cracks with temperature.The mechanism of strength behaviour is elucidated in detail based on fracture propagation analysis and the anisotropic response of with or,without grouted samples.展开更多
Geotechnical engineering usually produces drillholes in the ground for investigation and construction.Drilling is a rock-breaking process by applying normal(thrust)and shear(torque)force from the drill bit to the rock...Geotechnical engineering usually produces drillholes in the ground for investigation and construction.Drilling is a rock-breaking process by applying normal(thrust)and shear(torque)force from the drill bit to the rock below the bit.These rock-breaking data can be obtained by digital monitoring and recording the drilling parameters through an instrumented drilling machine.However,there is no mature and standard method to determine rock strength properties(such as unconfined compressive strength,UCS,or tensile strength)from real-time monitored drilling parameter(such as thrust force,torque,rotation speed,drilling speed and specific energy).This paper presents a complete procedure to accurately determine each drilling parameter.More importantly,the specific energy develops nonlinearly with change of the thrust force,which is related to the UCS and tensile strength of the rock.This finding provides an insight into determining the UCS and tensile strength of the rock based on real-time monitored drilling parameters.In addition,novel test setups are demonstrated to determine the thrust force and torque from hydraulics pressures and rotation speeds.These setups can significantly reduce the sophisticated instrumentation cost for drilling monitoring studies.Three type rocks including granite,limestone and sandstone are used for the testing.The findings from this study provide supporting theories to upgrade drilling monitoring technique to a standard geotechnical testing method.展开更多
The strength of backfill body is a crucial parameter in backfilling mining,and the failure process of cemented backfill body is essentially an energy dissipation process.To investigate the effects of curing age and ce...The strength of backfill body is a crucial parameter in backfilling mining,and the failure process of cemented backfill body is essentially an energy dissipation process.To investigate the effects of curing age and cement-sand ratio on the strength and energy consumption of backfill,whole tailings were used as aggregate to prepare slurry with mass concentration of 74%,and the slurry with cement-sand ratio of 1:4,1:6,1:8 and 1:12 was poured into backfill.Uniaxial compression tests were conducted on backfill body specimens that had been cured for 7 days,14 days,28 days,and 45 days.It aims at studying the compressive strength,damage,energy storage limit,energy dissipation,and crack propagation of the fill.The results show that when the cement-sand ratio is held constant,the strength of the backfill increases with curing age.Simultaneously,when the curing age is fixed,the strength is positively correlated with the cement-sand ratio.During uniaxial compression tests,it is observed that the pre-peak energy consumption,post-peak energy consumption,total energy consumption,and unit volume strain energy of the cemented backfill body exhibit exponential relationships with both curing age and cement-sand ratio.The energy storage limit of the backfill reflects its capacity to absorb energy prior to failure,while the relationship between damage and energy consumption provides an accurate depiction of its internal failure mechanisms at different stages.In the failure process of the cemented backfill body,primary cracks accompany secondary cracks,many microcracks initiate and propagate from the stress direction,and crack propagation consumes a significant amount of energy.This study on the strength,energy storage limit,and failure of the cemented backfill body can provide valuable insights for mine safety production.展开更多
基金The Natural Science Foundation of Jiangsu Province(No.BK2011618)the National Key Technology R&D Program of China during the12th Five-Year Plan Period(No.2012BAJ01B02)
文摘This study aims to quantify the influence of the amount of cement and chloride salt on the unconfined compression strength （UCS） of Lianyungang marine clay. The clays with various sodium chloride salt concentrations were prepared artificially and stabilized by ordinary Portland cement with various contents. A series of UCS tests of cement stabilized clay specimen after 28 d curing were carried out. The results indicate that the increase of salt concentration results in the decrease in the UCS of cement-treated soil. The negative effect of salt concentration on the strength of cement stabilized clay directly relates to the cement content and salt concentration. The porosity-salt concentration/cement content ratio is a fundamental parameter for assessing the UCS of cement-treated salt-rich clay. An empirical prediction model of UCS is also proposed to take into account the effect of salt concentration. The findings of this study can be referenced for the stabilization improvement of chloride slat- rich soft clay.
基金supported by the National Natural Science Foundation of China(No.51905417)China Postdoctoral Science Foundation(No.2020M670306).
文摘New-type magnesium alloy with prominent solubility and mechanical property lays foundation for preparing fracturing part in petroleum extraction.Herein,Mg-xZn-Zr-SiC alloy is prepared with casting strategy.Electrochemical and compression tests are conducted to assess the feasibility as decomposable material.Morphology,composition,phase and distribution are characterized to investigate decomposition mechanism.Results indicate that floccule,substrate component and reticulate secondary phase are formed on as-prepared surface.Sample also acts out enhanced compression strength to maintain pressure and guarantee stability in dissolution process.Furthermore,as decomposition time and zinc content increase,couple corrosion intensifies,resulting in gradually enhanced decomposition rate.Rapid sample decomposition is mainly due to basal anode dissolution,micro particle exfoliation and poor decomposition resistance of corroding product.Such work shows profound significance in preparing new-type accessible alloy to ensure rapid dissolution of fracturing part and guarantee stable compression strength in oil-gas reservoir exploitation.
文摘Quantitatively correcting the unconfined compressive strength for sample disturbance is an important research project in the practice of ocean engineering and geotechnical engineering. In this study, the specimens of undisturbed natural marine clay obtained from the same depth at the same site were deliberately disturbed to different levels. Then, the specimens with different extents of sample disturbance were trimmed for both oedometer tests and unconfined compression tests. The degree of sample disturbance SD is obtained from the oedometer test data. The relationship between the unconfined compressive strength q u and SD is studied for investigating the effect of sample disturbance on q u. It is found that the value of q u decreases linearly with the increase in SD. Then, a simple method of correcting q u for sample disturbance is proposed. Its validity is also verified through analysis of the existing published data.
基金financial support from the Chilean National Agency for Research and Development(ANID),National Doctorate No.21212028financial support from ANID,FONDECYT Regular Research Project No.1221793.
文摘Rubberized concrete is one of the most studied applications of discarded tires and offers a promising approach to developing materials with enhanced properties.The rubberized concrete mixture results in a reduced modulus of elasticity and a reduced compressive and tensile strength compared to traditional concrete.This study employs finite element simulations to investigate the elastic properties of rubberized mortar(RuM),considering the influence of inclusion stiffness and interfacial debonding.Different homogenization schemes,including Voigt,Reuss,and mean-field approaches,are implemented using DIGIMAT and ANSYS.Furthermore,the influence of the interfacial transition zone(ITZ)between mortar and rubber is analyzed by periodic homogenization.Subsequently,the influence of the ITZ is examined through a linear fracture analysis with the stress intensity factor as a key parameter,using the ANSYS SMART crack growth tool.Finally,a non-linear study in FEniCS is carried out to predict the strength of the composite material through a compression test.Comparisons with high density polyethylene(HDPE)and gravel inclusions show that increasing inclusion stiffness enhances compressive strength far more effectively than simply improving the mortar/rubber bond.Indeed,when the inclusions are much softer than the surrounding matrix,any benefit gained on the elastic modulus or strength from stronger interfacial adhesion becomes almost negligible.This study provide numerical evidence that tailoring the rubber’s intrinsic stiffness—not merely strengthening the rubber/mortar interface—is a decisive factor for improving the mechanical performance of RuM.
基金Supported by Yunnan Major Scientific and Technological Projects(No.202403AA080001)National Natural Science Foundation of China(No.52074137)Yunnan Fundamental Research Projects(No.202201AT070151)。
文摘Three types of activators such as sodium hydroxide,calcium oxide and triethanolamine(TEA)are used to establish different activation environments to address the problems associated with the process of activating fly ash paste.We conducted mechanical tests and numerical simulations to understand the evolution of microstructure,and used environmental scanning electron microscopy(ESEM)and energy dispersive spectroscopy(EDS)techniques to analyze the microenvironments of the samples.The mechanical properties of fly ash paste under different activation conditions and the changes in the microstructure and composition were investigated.The results revealed that under conditions of low NaOH content(1%-3%),the strength of the sample increased significantly.When the content exceeded 4%,the rate of increase in strength decreased.Based on the results,the optimal NaOH content was identified,which was about 4%.A good activation effect,especially for short-term activation(3-7 d),was achieved using TEA under high doping conditions.The activation effect was poor for long-term strength after 28 days.The CaO content did not significantly affect the degree of activation achieved.The maximum effect was exerted when the content of CaO was 2%.The virtual cement and concrete testing laboratory(VCCTL)was used to simulate the hydration process,and the results revealed that the use of the three types of activators accelerated the formation of Ca(OH)_(2) in the system.The activators also corroded the surface of the fly ash particles,resulting in a pozzolanic reaction.The active substances in fly ash were released efficiently,and hydration was realized.The pores were filled with hydration products,and the microstructure changed to form a new frame of paste filling that helped improve the strength of fly ash paste.
基金Funded by the Natural Science Foundation of China(No.52109168)。
文摘In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive strength.In this study,505 groups of data were collected,and a new database of compressive strength of PFGC was constructed.In order to establish an accurate prediction model of compressive strength,five different types of machine learning networks were used for comparative analysis.The five machine learning models all showed good compressive strength prediction performance on PFGC.Among them,R2,MSE,RMSE and MAE of decision tree model(DT)are 0.99,1.58,1.25,and 0.25,respectively.While R2,MSE,RMSE and MAE of random forest model(RF)are 0.97,5.17,2.27 and 1.38,respectively.The two models have high prediction accuracy and outstanding generalization ability.In order to enhance the interpretability of model decision-making,we used importance ranking to obtain the perception of machine learning model to 13 variables.These 13 variables include chemical composition of fly ash(SiO_(2)/Al_(2)O_(3),Si/Al),the ratio of alkaline liquid to the binder,curing temperature,curing durations inside oven,fly ash dosage,fine aggregate dosage,coarse aggregate dosage,extra water dosage and sodium hydroxide dosage.Curing temperature,specimen ages and curing durations inside oven have the greatest influence on the prediction results,indicating that curing conditions have more prominent influence on the compressive strength of PFGC than ordinary Portland cement concrete.The importance of curing conditions of PFGC even exceeds that of the concrete mix proportion,due to the low reactivity of pure fly ash.
基金Funded by China National Key Research and Development Program for Application and Verification of Typical Groundwater Contaminated Sites(No.2019YFC1804805)Shenyang Key Laboratory of Safety Evaluation and Disaster Prevention of Engineering Structures(No.S230184)the Funding Project of Northeast Geological S&T Innovation Center of China Geological Survey(No.QCJJ2023-39)。
文摘Traditional machine learning(ML)encounters the challenge of parameter adjustment when predicting the compressive strength of reclaimed concrete.To address this issue,we introduce two optimized hybrid models:the Bayesian optimization model(B-RF)and the optimal model(Stacking model).These models are applied to a data set comprising 438 observations with five input variables,with the aim of predicting the compressive strength of reclaimed concrete.Furthermore,we evaluate the performance of the optimized models in comparison to traditional machine learning models,such as support vector regression(SVR),decision tree(DT),and random forest(RF).The results reveal that the Stacking model exhibits superior predictive performance,with evaluation indices including R2=0.825,MAE=2.818 and MSE=14.265,surpassing the traditional models.Moreover,we also performed a characteristic importance analysis on the input variables,and we concluded that cement had the greatest influence on the compressive strength of reclaimed concrete,followed by water.Therefore,the Stacking model can be recommended as a compressive strength prediction tool to partially replace laboratory compressive strength testing,resulting in time and cost savings.
文摘Introduction It is necessary for an ideal bioceramic scaffold to have a suitable structure.The structure can affect the mechanical properties of the scaffold(i.e.,elastic modulus and compressive strength)and the biological properties of the scaffold(i.e.,degradability and cell growth rate).Lattice structure is a kind of periodic porous structure,which has some advantages of light weight and high strength,and is widely used in the preparation of bioceramic scaffolders.For the structure of the scaffold,high porosity and large pore size are important for bone growth,bone integration and promoting good mechanical interlocking between neighboring bones and the scaffold.However,scaffolds with a high porosity often lack mechanical strength.In addition,different parts of the bone have different structural requirements.In this paper,scaffolds with a non-uniform structure or a hierarchical structure were designed,with loose and porous exterior to facilitate cell adhesion,osteogenic differentiation and vascularization as well as relatively dense interior to provide sufficient mechanical support for bone repair.Methods In this work,composite ceramics scaffolds with 10%akermanite content were prepared by DLP technology.The scaffold had a high porosity outside to promote the growth of bone tissue,and a low porosity inside to withstand external forces.The compressive strength,fracture form,in-vitro degradation performance and bioactivity of graded bioceramic scaffolds were investigated.The models of scaffolds were imported into the DLP printer with a 405 nm light.The samples were printed with the intensity of 8 mJ/cm^(2)and a layer thickness of 50μm.Finally,the ceramic samples were sintered at 1100℃.The degradability of the hierarchical gyroid bioceramic scaffolds was evaluated through immersion in Tris-HCl solution and SBF solution at a ratio of 200 mL/g.The bioactivity of bioceramic was obtained via immersing them in SBF solution for two weeks.The concentrations of calcium,phosphate,silicon,and magnesium ions in the soaking solution were determined by an inductively coupled plasma optical emission spectrometer.Results and discussion In this work,a hierarchical Gyroid structure HA-AK10 scaffold(sintered at 1100℃)with a radial internal porosity of 50%and an external porosity of 70%is prepared,and the influence of structural form on the compressive strength and degradation performance of the scaffold is investigated.The biological activity of the bioceramics in vitro is also verified.The mechanical simulation results show that the stress distribution corresponds to the porosity distribution of the structure,and the low porosity is larger and the overall stress concentration phenomenon does not appear.After soaking in SBF solution,Si—OH is firstly formed on the surface of bioceramics,and then silicon gel layer is produced due to the presence of calcium and silicon ions.The silicon gel layer is dissociated into negatively charged groups under alkaline environment secondary adsorption of calcium ions and phosphate ions,forming amorphous calcium phosphate,and finally amorphous calcium phosphate crystals and adsorption of carbonate ions,forming carbonate hydroxyapatite.This indicates that the composite bioceramics have a good biological activity in-vitro and can provide a good environment for the growth of bone cells.A hierarchical Gyroid ceramic scaffold with a bone geometry is prepared via applying the hierarchical structure to the bone contour scaffold.The maximum load capacity of the hierarchical Gyroid ceramic scaffold is 8 times that of the uniform structure.Conclusions The hierarchical structure scaffold designed had good overall compressive performance,good degradation performance,and still maintained a good mechanical stability during degradation.In addition,in-vitro biological experimental results showed that the surface graded composite scaffold could have a good in-vitro biological activity and provide a good environment for bone cells.Compared to the heterosexual structure,the graded scaffold had greater mechanical properties.
文摘Microwave-curing and mechanical grinding of fly ash have both beenadopted as effective methods for improving the early-age strength of alkali-activated fly ash(AAFA)binders.This study combined these two approaches by synthesizing AAFA using original,medium-fine,and ultrafine fly ash as precursors,and then specimens were cured with a five-stage temperature-controlled microwave.The compressive strength results indicate that the original AAFA develops the highest strength initially during microwave-curing,reaching 28 MPa at stage 2.Medium-fine AAFA exhibits the highest strength of 60 MPa when cured to stage 4-I,which is 26%higher than the peak strength of original AAFA.It is attributed to the significant rise in their specific surface area,which accelerates the dissolution of Si and Al from the precursor and facilitates the subsequent formation of N-A-S-H gels.Additionally,nanoscale zeolite crystals formed as secondary products fill the tiny gaps between amorphous products,thereby significantly improving their microstructure.In contrast,ultrafine fly ash,primarily composed of fragmented particles,necessitated a substantial amount of water,which adversely affects the absorption efficiency for microwave of AAFA specimens.Thus,ultrafine AAFA specimens consistently exhibit the lowest compressive strength.Specifically,at the end of curing,the compressive strength of these three specimens with microwave-curing is approximately 32%,59%,and 172%higher than that of the steam-cured sample,respectively.These findings demonstrate the compatibility of microwave-curing and fly ash refinement in enhancing the early compressive strength development of AAFA.
基金supported by the National Key Research and Development Program of China(No.2023YFC3707002).
文摘The compressive strength of the pellets is a key indicator that determines the production efficiency in straight grate.It usually relies on manual sampling and testing,which is cumbersome and inefficient.To address this,a time series prediction model for pellet compressive strength was developed,combining a gradient boosting decision tree with a temporal convolutional network(GBDT-TCN).Firstly,the key physical characteristics of the pellet production process were established through the feature construction method,and then the multicollinear features were eliminated based on the Spearman correlation coefficient.The final selection of feature parameters,amounting to 9,was determined using recursive feature elimination(RFE)method.Finally,the GBDT algorithm was used to establish the nonlinear relationship between these features and the compressive strength.The GBDT prediction results and process data were constructed into a time series dataset,which was input into the TCN unit cascade model.The time series information was captured through the distribution coefficient of the loss function in the time series.Results illustrate that the GBDT-TCN method proposed performs well in the task of predicting the compressive strength of pellets.Compared with the prediction model using only GBDT,the accuracy within±100 N is increased from 83.33%to 90.00%.
基金funded by the National Natural Science Foundation of China(Grant 42177164)the Distinguished Youth Science Foundation of Hunan Province of China(2022JJ10073)supported by China Scholarship Council with the grant number of 202006370006.
文摘With the growing demand for sustainable development in the mining industry,cemented paste backfill(CPB)materials,primarily composed of tailings,play a crucial role in mine backfilling and underground support systems.To enhance the mechanical properties of CPB materials,fiber reinforcement technology has gradually gained attention,though challenges remain in predicting its performance.This study develops a hybrid model based on the adaptive equilibrium optimizer(adap-EO)-enhanced XGBoost method for accurately predicting the uniaxial compressive strength of fiber-reinforced CPB.Through systematic comparison with various other machine learning methods,results demonstrate that the proposed hybridmodel exhibits excellent predictive performance on the test set,achieving a coefficient of determination(R^(2))of 0.9675,root mean square error(RMSE)of 0.6084,and mean absolute error(MAE)of 0.4620.Input importance analysis reveals that cement-tailings ratio,curing time,and concentration are the three most critical factors affectingmaterial strength,with cement-tailings ratio showing a positive correlation with strength,concentrations above 70% significantly improvingmaterial strength,and curing periods beyond 28 days being essential for strength development.Fiber parameters contribute secondarily but notably to material strength,with fiber length exhibiting an optimal range of approximately 12 mm.This study not only provides a high-precision strength prediction model but also reveals the inherent correlations between various parameters and material performance,offering scientific basis for mixture optimization and engineering applications of fiber-reinforced CPB materials.
基金The support of Prince Sultan University for paying the Article Processing Charge(APC)of this publication and their support.Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R300).
文摘Soilcrete is a composite material of soil and cement that is highly valued in the construction industry.Accurate measurement of its mechanical properties is essential,but laboratory testing methods are expensive,timeconsuming,and include inaccuracies.Machine learning(ML)algorithms provide a more efficient alternative for this purpose,so after assessment with a statistical extraction method,ML algorithms including back-propagation neural network(BPNN),K-nearest neighbor(KNN),radial basis function(RBF),feed-forward neural networks(FFNN),and support vector regression(SVR)for predicting the uniaxial compressive strength(UCS)of soilcrete,were proposed in this study.The developed models in this study were optimized using an optimization technique,gradient descent(GD),throughout the analysis(direct optimization for neural networks and indirect optimization for other models corresponding to their hyperparameters).After doing laboratory analysis,data pre-preprocessing,and data-processing analysis,a database including 600 soilcrete specimens was gathered,which includes two different soil types(clay and limestone)and metakaolin as a mineral additive.80%of the database was used for the training set and 20%for testing,considering eight input parameters,including metakaolin content,soil type,superplasticizer content,water-to-binder ratio,shrinkage,binder,density,and ultrasonic velocity.The analysis showed that most algorithms performed well in the prediction,with BPNN,KNN,and RBF having higher accuracy compared to others(R^(2)=0.95,0.95,0.92,respectively).Based on this evaluation,it was observed that all models show an acceptable accuracy rate in prediction(RMSE:BPNN=0.11,FFNN=0.24,KNN=0.05,SVR=0.06,RBF=0.05,MAD:BPNN=0.006,FFNN=0.012,KNN=0.008,SVR=0.006,RBF=0.009).The ML importance ranking-sensitivity analysis indicated that all input parameters influence theUCS of soilcrete,especially the water-to-binder ratio and density,which have themost impact.
基金funded by the Natural Science Foundation of China(Grant No.52090084)was partially supported by the Sand Hazards and Opportunities for Resilience,Energy,and Sustainability(SHORES)Center,funded by Tamkeen under the NYUAD Research Institute Award CG013.
文摘This study focuses on empirical modeling of the strength characteristics of urban soils contaminated with heavy metals using machine learning tools and their subsequent stabilization with ordinary Portland cement(OPC).For dataset collection,an extensive experimental program was designed to estimate the unconfined compressive strength(Qu)of heavy metal-contaminated soils collected from awide range of land use pattern,i.e.residential,industrial and roadside soils.Accordingly,a robust comparison of predictive performances of four data-driven models including extreme learning machines(ELMs),gene expression programming(GEP),random forests(RFs),and multiple linear regression(MLR)has been presented.For completeness,a comprehensive experimental database has been established and partitioned into 80%for training and 20%for testing the developed models.Inputs included varying levels of heavy metals like Cd,Cu,Cr,Pb and Zn,along with OPC.The results revealed that the GEP model outperformed its counterparts:explaining approximately 96%of the variability in both training(R2=0.964)and testing phases(R^(2)=0.961),and thus achieving the lowest RMSE and MAE values.ELM performed commendably but was slightly less accurate than GEP whereas MLR had the lowest performance metrics.GEP also provided the benefit of traceable mathematical equation,enhancing its applicability not just as a predictive but also as an explanatory tool.Despite its insights,the study is limited by its focus on a specific set of heavy metals and urban soil samples of a particular region,which may affect the generalizability of the findings to different contamination profiles or environmental conditions.The study recommends GEP for predicting Qu in heavy metal-contaminated soils,and suggests further research to adapt these models to different environmental conditions.
基金supported by the National Natural Science Foundation of China(Grant No.12272029).
文摘Lumbar degeneration leads to changes in geometry and density distribution of vertebrae,which could further influence the mechanical property and behavior.This study aimed to quantitatively describe the variations in shape and density distribution for degenerated vertebrae by statistical models,and utilized the specific statistical shape model(SSM)/statistical appearance model(SAM)modes to assess compressive strength and fracture behavior.Highly detailed SSM and SAM were developed based on the 75 L1 vertebrae of elderly men,and their variations in shape and density distribution were quantified with principal component(PC)modes.All vertebrae were classified into mild(n=22),moderate(n=29),and severe(n=24)groups according to the overall degree of degeneration.Quantitative computed tomography-based finite element analysis was used to calculate compressive strength for each L1 vertebra,and the associations between compressive strength and PC modes were evaluated by multivariable linear regression(MLR).Moreover,the distributions of equivalent plastic strain(PEEQ)for the vertebrae assigned with the first modes of SSM and SAM at mean±3SD were investigated.The Leave-One-Out analysis showed that our SSM and SAM had good performance,with mean absolute errors of 0.335±0.084 mm and 64.610±26.620 mg/cm3,respectively.A reasonable accuracy of bone strength prediction was achieved by using four PC modes(SSM 1,SAM 1,SAM 4,and SAM 5)to construct the MLR model.Furthermore,the PEEQ values were more sensitive to degeneration-related variations of density distribution than those of morphology.The density variations may change the deformity type(compression deformity or wedge deformity),which further affects the fracture pattern.Statistical models can identify the morphology and density variations in degenerative vertebrae,and the SSM/SAM modes could be used to assess compressive strength and fracture behavior.The above findings have implications for assisting clinicians in pathological diagnosis,fracture risk assessment,implant design,and preoperative planning.
基金supported by the Postgraduate Innovation Program of Chongqing University of Science and Technology(Grant No.YKJCX2420605)Research Foundation of Chongqing University of Science and Technology(Grant No.ckrc20241225)+1 种基金Opening Projects of State Key Laboratory of Solid Waste Reuse for Building Materials(Grant No.SWR-2021-005)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJQN202401510)。
文摘Foam concrete is widely used in engineering due to its lightweight and high porosity.Its compressive strength,a key performance indicator,is influenced by multiple factors,showing nonlinear variation.As compressive strength tests for foam concrete take a long time,a fast and accurate prediction method is needed.In recent years,machine learning has become a powerful tool for predicting the compressive strength of cement-based materials.However,existing studies often use a limited number of input parameters,and the prediction accuracy of machine learning models under the influence of multiple parameters and nonlinearity remains unclear.This study selects foam concrete density,water-to-cement ratio(W/C),supplementary cementitious material replacement rate(SCM),fine aggregate to binder ratio(FA/Binder),superplasticizer content(SP),and age of the concrete(Age)as input parameters,with compressive strength as the output.Five different machine learning models were compared,and sensitivity analysis,based on Shapley Additive Explanations(SHAP),was used to assess the contribution of each input parameter.The results show that Gaussian Process Regression(GPR)outperforms the other models,with R2,RMSE,MAE,and MAPE values of 0.95,1.6,0.81,and 0.2,respectively.It is because GPR,optimized through Bayesian methods,better fits complex nonlinear relationships,especially considering a large number of input parameters.Sensitivity analysis indicates that the influence of input parameters on compressive strength decreases in the following order:foam concrete density,W/C,Age,FA/Binder,SP,and SCM.
基金support of the National Natural Science Foundation of China(52074080,52004001,and 51574002).
文摘Against the background of“carbon peak and carbon neutrality,”it is of great practical significance to develop non-blast furnace ironmaking technology for the sustainable development of steel industry.Carbon-bearing iron ore pellet is an innovative burden of direct reduction ironmaking due to its excellent self-reducing property,and the thermal strength of pellet is a crucial metallurgical property that affects its wide application.The carbon-bearing iron ore pellet without binders(CIPWB)was prepared using iron concentrate and anthracite,and the effects of reducing agent addition amount,size of pellet,reduction temperature and time on the thermal compressive strength of CIPWB during the reduction process were studied.Simultaneously,the mechanism of the thermal strength evolution of CIPWB was revealed.The results showed that during the low-temperature reduction process(300-500℃),the thermal compressive strength of CIPWB linearly increases with increasing the size of pellet,while it gradually decreases with increasing the anthracite ratio.When the CIPWB with 8%anthracite is reduced at 300℃for 60 min,the thermal strength of pellet is enhanced from 13.24 to 31.88 N as the size of pellet increases from 8.04 to 12.78 mm.Meanwhile,as the temperature is 500℃,with increasing the anthracite ratio from 2%to 8%,the thermal compressive strength of pellet under reduction for 60 min remarkably decreases from 41.47 to 8.94 N.Furthermore,in the high-temperature reduction process(600-1150℃),the thermal compressive strength of CIPWB firstly increases and then reduces with increasing the temperature,while it as well as the temperature corresponding to the maximum strength decreases with increasing the anthracite ratio.With adding 18%anthracite,the thermal compressive strength of pellet reaches the maximum value at 800℃,namely 35.00 N,and obtains the minimum value at 1050℃,namely 8.60 N.The thermal compressive strength of CIPWB significantly depends on the temperature,reducing agent dosage,and pellet size.
基金supported by the State Scholarship Fund from the China Scholarship Council(CSC)No.202006180076.
文摘Microbially induced carbonate precipitation(MICP)is an eco-friendly soil improvement technique.However,this method still has some drawbacks,such as low conversion efficiency of CaCO_(3) crystallization,insufficient strength for certain applications,and requiring multiple treatments.Previous studies have re-ported that sticky rice can regulate CaCO_(3) crystals(i.e.,chemical CaCO_(3))in the sticky rice-lime mortar,showing potential for improving the bio-cementation.Therefore,this study explored the possibility of using sticky rice to enhance the biocementation effect.Tests were carried out to assess the strength and perme-ability of bio-cemented sand with the inclusion of sticky rice.The results indicated that sticky rice may regulate the type and size of bio-CaCO_(3) crystals,and the use of an appropriate amount of sticky rice as additive could increase the strength of sand columns by regulating CaCO_(3) crystallization.Polyhedral calcites may be more favourable for the increasing strength than some vaterites with a hollow spherical structure.The combination of MICP and sticky rice can significantly decrease the coefficient of permeability to a value that was much lower than that by using sticky rice and MICP alone.Bio-CaCO_(3) immobilized the sticky rice on one end on sand particles,and the reticulated structure of sticky rice divided large pores into small pores,which may be the important cause of the decrease in permeability coefficient.Finally,this study proposed that the MICP with the sticky rice as an additive may enhance the MICP effect and prevent the surface erosion of coarse-grained sand slopes.
文摘This study investigates the effect of different in situ conditions like flaw infill,heat-treatment temperatures,and sample porosities on the anisotropic compressive response of jointed samples with an impersistent flaw.Jointed samples of different porosities are prepared by mixing Plaster of Paris(POP)with different water contents,i.e.60%(i.e.for lower porosity)and 80%(i.e.for higher porosity).These samples are grouted with different infill materials,i.e.un-grouted,cement and sand-cement(3:1)-bio-concrete(SCB)mix and subsequently subjected to different temperatures,i.e.100℃,200℃ and 300℃.The results reveal the distinct stages in the stress-strain responses of samples characterized by initial micro-cracks closure,elastic transition,and non-linear response till peak followed by a post-peak behaviour.The un-grouted samples exhibit their lowest strength at 30°joint orientation.The ratios of maximum to minimum strength are 3.11 and 3.22 with varying joint orientations for lower and higher porosity samples,respectively.Strengths of cement and SCB mix grouted samples are increased for all joint orientations ranging between 16.13%-69.83%and 18.04%-73%at low porosity and 22%-48.66%and 27.77%-51.57%at high porosity,respectively as compared to the un-grouted samples.However,the strength of the grouted samples is decreased by 66.94%-75.47%and 77.17%-81.05%at lower porosity,and 79.37%-82.86%and 81.29%-95.55%at higher porosity for cement and for SCB grouts with an increase in the heating temperature from 30℃ to 300℃,respectively.These observations could be due to the suppression of favourable crack initiation locations,i.e.flaw tips along the samples due to the filling of the crack by grouting and generation of thermal cracks with temperature.The mechanism of strength behaviour is elucidated in detail based on fracture propagation analysis and the anisotropic response of with or,without grouted samples.
基金supported by the National Natural Science Foundation of China(Grant Nos.42272338,41902275)the Sichuan Transportation Science and Technology Program(Grant No.2018-ZL-02).
文摘Geotechnical engineering usually produces drillholes in the ground for investigation and construction.Drilling is a rock-breaking process by applying normal(thrust)and shear(torque)force from the drill bit to the rock below the bit.These rock-breaking data can be obtained by digital monitoring and recording the drilling parameters through an instrumented drilling machine.However,there is no mature and standard method to determine rock strength properties(such as unconfined compressive strength,UCS,or tensile strength)from real-time monitored drilling parameter(such as thrust force,torque,rotation speed,drilling speed and specific energy).This paper presents a complete procedure to accurately determine each drilling parameter.More importantly,the specific energy develops nonlinearly with change of the thrust force,which is related to the UCS and tensile strength of the rock.This finding provides an insight into determining the UCS and tensile strength of the rock based on real-time monitored drilling parameters.In addition,novel test setups are demonstrated to determine the thrust force and torque from hydraulics pressures and rotation speeds.These setups can significantly reduce the sophisticated instrumentation cost for drilling monitoring studies.Three type rocks including granite,limestone and sandstone are used for the testing.The findings from this study provide supporting theories to upgrade drilling monitoring technique to a standard geotechnical testing method.
基金funded by the National Natural Science Foundation of China(52474131)the National Natural Science Foundation of China(42467022)+1 种基金the Yunnan Major Scientific and Technological Projects(Grant No.202202AG050014)the Yunnan Fundamental Research Projects(NO.202101BE070001-038,202201AT070146).
文摘The strength of backfill body is a crucial parameter in backfilling mining,and the failure process of cemented backfill body is essentially an energy dissipation process.To investigate the effects of curing age and cement-sand ratio on the strength and energy consumption of backfill,whole tailings were used as aggregate to prepare slurry with mass concentration of 74%,and the slurry with cement-sand ratio of 1:4,1:6,1:8 and 1:12 was poured into backfill.Uniaxial compression tests were conducted on backfill body specimens that had been cured for 7 days,14 days,28 days,and 45 days.It aims at studying the compressive strength,damage,energy storage limit,energy dissipation,and crack propagation of the fill.The results show that when the cement-sand ratio is held constant,the strength of the backfill increases with curing age.Simultaneously,when the curing age is fixed,the strength is positively correlated with the cement-sand ratio.During uniaxial compression tests,it is observed that the pre-peak energy consumption,post-peak energy consumption,total energy consumption,and unit volume strain energy of the cemented backfill body exhibit exponential relationships with both curing age and cement-sand ratio.The energy storage limit of the backfill reflects its capacity to absorb energy prior to failure,while the relationship between damage and energy consumption provides an accurate depiction of its internal failure mechanisms at different stages.In the failure process of the cemented backfill body,primary cracks accompany secondary cracks,many microcracks initiate and propagate from the stress direction,and crack propagation consumes a significant amount of energy.This study on the strength,energy storage limit,and failure of the cemented backfill body can provide valuable insights for mine safety production.
